Fix Python – How to apply gradient clipping in TensorFlow?

Considering the example code.
I would like to know How to apply gradient clipping on this network on the RNN where there is a possibility of exploding gradients.
tf.clip_by_value(t, clip_value_min, clip_value_max, name=None)

This is an example that could be used but where do I introduce this ?
In the def of RNN
lstm_cell = rnn_cell.BasicLSTM….

Fix Python – TensorFlow, “‘module’ object has no attribute ‘placeholder'”

I’ve been trying to use tensorflow for two days now installing and reinstalling it over and over again in python2.7 and 3.4. No matter what I do, I get this error message when trying to use tensorflow.placeholder()
It’s very boilerplate code:
tf_in = tf.placeholder(“float”, [None, A]) # Features

No matter what I do I always get the trace back:

Fix Python – Can Keras with Tensorflow backend be forced to use CPU or GPU at will?

I have Keras installed with the Tensorflow backend and CUDA. I’d like to sometimes on demand force Keras to use CPU. Can this be done without say installing a separate CPU-only Tensorflow in a virtual environment? If so how? If the backend were Theano, the flags could be set, but I have not heard of Tensorflow flags accessible via Keras.

Fix Python – How to load a model from an HDF5 file in Keras?

How to load a model from an HDF5 file in Keras?
What I tried:
model = Sequential()

model.add(Dense(64, input_dim=14, init=’uniform’))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))

model.add(Dense(64, init=’uniform’))

Fix Python – Google Colaboratory: misleading information about its GPU (only 5% RAM available to some users)

update: this question is related to Google Colab’s “Notebook settings: Hardware accelerator: GPU”. This question was written before the “TPU” option was added.
Reading multiple excited announcements about Google Colaboratory providing free Tesla K80 GPU, I tried to run lesson on it for it to never complete – quickly running out of memory. ….

Fix Python – Deep-Learning Nan loss reasons

Perhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to diverge?
I am using Tensorflow’s iris_training model with some of my own data and keep getting

ERROR:tensorflow:Model diverged with loss = NaN.

Fix Python – RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

for data in dataloader:
inputs, labels = data
outputs = model(inputs)

Gives the error:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same


Fix Python – What are the pros and cons between get_dummies (Pandas) and OneHotEncoder (Scikit-learn)?

I’m learning different methods to convert categorical variables to numeric for machine-learning classifiers. I came across the pd.get_dummies method and sklearn.preprocessing.OneHotEncoder() and I wanted to see how they differed in terms of performance and usage.
I found a tutorial on how to use OneHotEncoder() on….

Fix Python – How to compute precision, recall, accuracy and f1-score for the multiclass case with scikit learn?

I’m working in a sentiment analysis problem the data looks like this:
label instances
5 1190
4 838
3 239
1 204
2 127

So my data is unbalanced since 1190 instances are labeled with 5. For the classification Im using scikit’s SVC. The problem is I do not know how to balance my data in the right way in order to….